Fix incorrect "is" comparisons to instead use "==".

PiperOrigin-RevId: 237140147

tensorflow/contrib/gan/python/features/python/virtual_batchnorm_test.py

@@ -112,7 +112,7 @@ class VirtualBatchnormTest(test.TestCase):
           batch, axis, training=True)
 
       # Get VBN's batch normalization on reference batch.
-      batch_axis = 0 if axis is not 0 else 1  # axis and batch_axis can't same
+      batch_axis = 0 if axis != 0 else 1  # axis and batch_axis can't same
       vbn = virtual_batchnorm.VBN(batch, axis, batch_axis=batch_axis)
       vbn_normalized = vbn.reference_batch_normalization()
 

tensorflow/lite/experimental/micro/examples/micro_speech/apollo3/captured_data_to_wav.py

@@ -28,7 +28,7 @@ def new_data_to_array(fn):
   vals = []
   with open(fn) as f:
     for n, line in enumerate(f):
-      if n is not 0:
+      if n != 0:
         vals.extend([int(v, 16) for v in line.split()])
   b = ''.join(map(chr, vals))
   y = struct.unpack('<' + 'h' * int(len(b) / 2), b)

tensorflow/lite/experimental/micro/examples/micro_speech/apollo3/compare_1k.py

@@ -30,7 +30,7 @@ def new_data_to_array(fn, datatype='int16'):
   vals = []
   with open(fn) as f:
     for n, line in enumerate(f):
-      if n is not 0:
+      if n != 0:
         vals.extend([int(v, 16) for v in line.split()])
   b = ''.join(map(chr, vals))
 

tensorflow/python/data/experimental/kernel_tests/stats_dataset_ops_test.py

@@ -157,7 +157,7 @@ class StatsDatasetTest(stats_dataset_test_base.StatsDatasetTestBase):
     for i in range(34):
       self.assertEqual(i * 3, self.evaluate(next_element()))
       summary_str = self.evaluate(aggregator.get_summary())
-      if i is not 0:
+      if i != 0:
         self._assertSummaryHasScalarValue(
             summary_str,
             self.regexForNodeName("FilterDataset", "dropped_elements"),

tensorflow/python/distribute/mirrored_strategy.py

@@ -319,7 +319,7 @@ def _cluster_spec_to_device_list(cluster_spec, num_gpus_per_worker):
   devices = []
   for task_type in ("chief", "worker"):
     for task_id in range(len(cluster_spec.as_dict().get(task_type, []))):
-      if num_gpus_per_worker is 0:
+      if num_gpus_per_worker == 0:
         devices.append("/job:%s/task:%d" % (task_type, task_id))
       else:
         devices.extend([

tensorflow/python/grappler/cost_analyzer_test.py

@@ -124,7 +124,7 @@ class CostAnalysisTest(test.TestCase):
       op_count = int(m.group(1))
       # upper = int(m.group(5))
       lower = int(m.group(6))
-      if op_type is b"MatMul":
+      if op_type == b"MatMul":
         self.assertEqual(3, op_count)
       else:
         self.assertEqual(1, op_count)

tensorflow/python/kernel_tests/softmax_op_test.py

@@ -34,7 +34,7 @@ from tensorflow.python.platform import tf_logging as logging
 class SoftmaxTest(test.TestCase):
 
   def _npSoftmax(self, features, dim=-1, log=False):
-    if dim is -1:
+    if dim == -1:
       dim = len(features.shape) - 1
     one_only_on_dim = list(features.shape)
     one_only_on_dim[dim] = 1

tensorflow/python/kernel_tests/xent_op_test.py

@@ -41,7 +41,7 @@ from tensorflow.python.platform import test
 class XentTest(test.TestCase):
 
   def _npXent(self, features, labels, dim=-1):
-    if dim is -1:
+    if dim == -1:
       dim = len(features.shape) - 1
     one_only_on_dim = list(features.shape)
     one_only_on_dim[dim] = 1

tensorflow/python/ops/ragged/ragged_batch_gather_with_default_op.py

@@ -154,7 +154,7 @@ def _get_pad_shape(params, indices):
   if params.shape.ndims == indices.shape.ndims:
     # When params and indices are the same rank, the shape of the pad tensor is
     # almost identical to params, except the last dimension which has size = 1.
-    if params_shape.num_inner_dimensions is 0:
+    if params_shape.num_inner_dimensions == 0:
       pad_dims = params_shape.partitioned_dim_sizes[:-1] + (
           array_ops.ones_like(params_shape.partitioned_dim_sizes[-1]),)
       return ragged_tensor_shape.RaggedTensorDynamicShape(

tensorflow/python/summary/plugin_asset.py

@@ -67,7 +67,7 @@ def get_plugin_asset(plugin_asset_cls, graph=None):
   name = _PLUGIN_ASSET_PREFIX + plugin_asset_cls.plugin_name
   container = graph.get_collection(name)
   if container:
-    if len(container) is not 1:
+    if len(container) != 1:
       raise ValueError("Collection for %s had %d items, expected 1" %
                        (name, len(container)))
     instance = container[0]
@@ -102,7 +102,7 @@ def get_all_plugin_assets(graph=None):
   out = []
   for name in graph.get_collection(_PLUGIN_ASSET_PREFIX):
     collection = graph.get_collection(_PLUGIN_ASSET_PREFIX + name)
-    if len(collection) is not 1:
+    if len(collection) != 1:
       raise ValueError("Collection for %s had %d items, expected 1" %
                        (name, len(collection)))
     out.append(collection[0])